1. Proceedings
  2. I3M
  3. 2018
  4. SESDE
  5. 10.46354/i3m.2018.sesde.009

Modelling and control of a photovoltaic energy system with battery storage

  • a Carlos Y. García-Ramos,  
  • aJose M. González-Cava,  
  • b José F. Gómez González, 
  • b Sara González Pérez, 
  • b Benjamín González Díaz, 
  • a Juan Albino Méndez-Pérez
  • aDep. of Computer Science and Systems Engineering, Universidad de La Laguna. APDO. 456, 38200 La Laguna,Tenerife, SPAIN
  • bDep. of Industrial Engineering, Universidad de La Laguna. APDO. 456, 38200 La Laguna, Tenerife, SPAIN
Cite as
García-Ramos C.Y., González-Cava J.M., Gómez González J.F., González Pérez S.,
González Díaz B., Méndez-Pérez J.A. (2018). Modelling and control of a photovoltaic energy system with battery storage. Proceedings of the 6th International Workshop on Simulation for Energy, Sustainable Development & Environment (SESDE 2018), pp. 56-61. DOI: https://doi.org/10.46354/i3m.2018.sesde.009

Abstract

This work presents a simulated study of the energy management of an energy system connected to the grid with photovoltaic generation and battery storage. The work proposes a energy management system based on fuzzy logic. It is intended to be used in the hotel industry. The objective of the proposed controller is to maximise the renewable power source but including also economic criteria in the management. The proposal was implemented in simulation considering a 5,1kW peak photovoltaic installation and a set of batteries with a capacity of 384Ah. First results obtained show that the system achieves the specifications proposed. Thus, the study evidences the potential of the proposed control algorithm and demonstrate the suitability of the use of intelligent techniques for the energy management in hotels.

References

  1. Cabello Eras, J. J., Sousa Santos, V., Sagastume Gutiérrez, A., Guerra Plasencia, M. Á., Haeseldonckx, D., & Vandecasteele, C., 2016. Tools to improve forecasting and control of the electricity consumption in hotels. Journal of Cleaner Production, 137, 803–812. https://doi.org/10.1016/j.jclepro.2016.07.192
  2. Comodi, G., Renzi, M., Cioccolanti, L., Caresana, F., & Pelagalli, L., 2015. Hybrid system with micro gas turbine and PV (photovoltaic) plant: Guidelines for sizing and management strategies. Energy, 89, 226–235. https://doi.org/10.1016/j.energy.2015.07.072
  3. Geaney, H., & O’Dwyer, C. (2017). Tailoring Asymmetric Discharge- Charge Rates and Capacity Limits to Extend Li-O2 Battery Cycle Life. ChemElectroChem, 4(3), 628–635. https://doi.org/10.1002/celc.201600662
  4. González Rodríguez, G. C., Méndez, J. A., Batista, B. M., & Gonzalez- Cava, J. M., 2018. A Fuzzy Modelling Approach to Laundry Industry. In J. Kacprzyk, E. Szmidt, S. Zadrożny, K. T. Atanassov, & M. Krawczak (Eds.), Advances in Fuzzy Logic and Technology 2017: Proceedings of: EUSFLAT 2017 Springer International Publishing. https://doi.org/10.1007/978-3-319-66824-6_14.
  5. Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., Nor, K. M. D., & Khoshnoudi, M., 2016. Using fuzzy multiple criteria decision making approaches for evaluating energy saving
    technologies and solutions in five star hotels: A new hierarchical framework. Energy, 117, 131–148. https://doi.org/10.1016/j.energy.2016.10.076
  6. Hernández-Magallanes J.A., Domínguez-Inzunza L.A., Gutiérrez-Urueta G., Soto P., Jiménez C., Rivera W., 2014. Experimental assessment of an absorption cooling system operating with the ammonia/lithium nitrate mixture. Energy 78, 685-692.
  7. Mendez, J. A., Leon, A., Marrero, A., Gonzalez-Cava, J. M., Reboso, J. A., Estevez, J. I., & Gomez-Gonzalez, J. F., 2018. Improving the anesthetic process by a fuzzy rule based medical decision system. Artificial Intelligence in Medicine, 84, 159–170. https://doi.org/10.1016/j.artmed.2017.12.005
  8. Mohamed, A., & Mohammed, O., 2013. Real-time energy management scheme for hybrid renewable energy systems in smart grid applications. Electric Power Systems Research, 96, 133–143. https://doi.org/10.1016/j.epsr.2012.10.015
  9. Olatomiwa, L., Mekhilef, S., Ismail, M. S., & Moghavvemi, M., 2016. Energy management strategies in hybrid renewable energy systems: A review. Renewable and Sustainable Energy Reviews, 62, 821–835. https://doi.org/10.1016/j.rser.2016.05.040
  10. Passino, K. M., & Yurkovich, S., 1998. Fuzzy Control. Addison- Wesley (Vol. 163). https://doi.org/10.3233/978-1-60750-706-2-39
  11. Wang J.F., Gao G.C., Chen G.M., 2000. An improved absorption refrigeration cycle driven by unsteady thermal sources below 100°C. Int. J. of Energy Research 24, 633-640.
  12. Real-Calvo, R., Moreno-Munoz, A., Pallares-Lopez, V., Gonzalez- Redondo, M. J., Moreno-Garcia, I. M., & Palacios-Garcia, E. J., 2017. Sistema Electrónico Inteligente para el Control de la Interconexión entre Equipamiento de Generación Distribuida y la Red Eléctrica. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, 14(1), 56–69. https://doi.org/10.1016/j.riai.2016.11.002
  13. Ruban, A. A. M., Rajasekaran, G. M., Pasupathi, T., & Rajeswari, N., 2016. A fuzzy-logic based management system in smartmicrogrid for residential applications. 1st International Conference on Emerging Trends in Engineering, Technology and Science, ICETETS 2016 - Proceedings. https://doi.org/10.1109/ICETETS.2016.7603096
  14. Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., & Bemporad, A., 2018. Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies, 11(3). https://doi.org/10.3390/en11030631
  15. Zadeh, L. A., 1994. Soft computing and fuzzy logic. Software, IEEE, (November), 48–56. https://doi.org/10.1109/52.329401